4 papers
Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG
Wei-Chieh Chou, Xuanjun Chen, Jian-Ren Lin +3
Multi-hop question answering remains challenging for Retrieval-Augmented Generation (RAG) because existing approaches either propagate errors across iterative retrieval rounds or o…
Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech
Xuanjun Chen, Yun-Shing Wu, Wei-Chung Lu +4
Recent neural audio codec-based speech generation (CodecFake) produces highly realistic audio, posing a challenge to existing deepfake countermeasure models. While using codec resy…
CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
Cheng-Yen Li, Xuanjun Chen, Claire Lin +4
Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While Retrieval-Augmented Generation…
A Preliminary Study of RAG for Taiwanese Historical Archives
Claire Lin, Bo-Han Feng, Xuanjun Chen +3
Retrieval-Augmented Generation (RAG) has emerged as a promising approach for knowledge-intensive tasks. However, few studies have examined RAG for Taiwanese Historical Archives. In…